Although more and more evidence supports CDC28 protein kinase subunit 1B (CKS1B) is involved significantly in the development of human cancers, most of the researches have focused on a single disease, and pan-cancer studies conducted from a holistic perspective of different tumor sources have not been reported yet. Here, for the first time, we investigated the potential oncogenic and prognostic role of CKS1B across 33 tumors based on public databases and further verified it in a small scale by RNA sequencing or quantitative real-time PCR. CKS1B was generally highly expressed in a majority of tumors and had a notable correlation with the prognosis of patients, but its prognostic significance in different tumors was not exactly the same. In addition, CKS1B expression was also closely related to the infiltration of cancer-associated fibroblasts in tumors such as breast invasive carcinoma, kidney chromophobe, lung adenocarcinoma, and tumor-infiltrating lymphocytes in tumors such as glioblastoma multiforme, bladder urothelial carcinoma, and brain lower grade glioma. Moreover, reduced CKS1B methylation was observed in certain tumors, for example, adrenocortical carcinoma. Cell cycle and kinase activity regulation and PI3K-Akt signaling pathway were found to be involved in the functional mechanism of CKS1B. In conclusion, our first pan-cancer analysis of CKS1B contributes to a better overall understanding of CKS1B and may provide a new target for future cancer therapy.
Recently, the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO) released the latest global cancer burden data for 2020, which estimated the incidence, mortality, and development trends of 36 cancer types in 185 countries. Based on this statistic, the number of new cancer cases worldwide in 2020 is estimated to be 19.29 million, of which 10.06 million are males and 9.23 million are females. The global cancer death in 2020 is estimated to be 9.96 million, of which 5.53 million are males and 4.43 million are females. On average, about 12,500 people every day, or about 8.7 people every minute, are diagnosed with cancer [
It is well known that the pathogenesis of cancer is very complex. Despite all the difficulties, scientists never give up fighting it. However, limited by various factors, such as small sample size, low statistical power, and poor repeatability, the application of many research results has encountered obstacles [
CDC28 protein kinase subunit 1B (CKS1B) is an indispensable regulatory unit of SCFSkp2 ubiquitin-linked enzyme complex, which promotes the binding of SCF to cyclin inhibitor P27 Kip1 and eventually degrades P27 Kip1, leading to the cell transition from G1 phase to S phase [
In this study, TCGA, ONCOMINE, and other databases were used for the first time to conduct a pan-cancer analysis of CKS1B. At the same time, we investigated the potential mechanisms of CKS1B in pathogenesis and clinical prognosis of different cancers in terms of gene expression, gene alteration, patient survival, DNA methylation, immune infiltration, and pathway enrichment.
The mRNA expression of CKS1B in different tumor types was analyzed in ONCOMINE database, under the settings of
The “Survival Map” and “Survival Analysis” module of GEPIA2 were used to make OS (overall survival) and DFS (disease-free survival) analysis diagrams of CKS1B across all TCGA tumors. The log-rank test was used for hypothesis testing, and the threshold was set as a Cox
Total RNA was extracted from bone marrow mononuclear cells of acute myeloid leukemia patients or hematopoietic stem cell transplantation donors using Trizol reagent (Ambion, Inc., Carlsbad, CA, USA). Samples were analyzed and quality controlled by BGI Gene Technology Company (China). After passing this test, cDNA library was constructed according to the TruSeq RNA Sample Preparation Kit (Illumina, San Diego, CA, USA). Each library was sequenced using single-reads on a HiSeq2000/1000 (Illumina). Cufflinks were used to measure gene expression levels in RPKM (reads per kilobase per million mapped reads).
Total RNA extraction of brain tissues from GEM patients and quantitative real-time PCR reaction was performed using Fast 200 Kit (Feijie Biotechnology Co., Ltd., Shanghai, China) and One Step TB Green PrimeScript RT-PCR kit (MBI Fermentas, St. Leon-Roth, Germany), respectively. The specific operation steps were carried out in accordance with instructions. Relative expression levels of transcription products were normalized to GAPDH. The primer sequences were used as CKS1B-F: 5
The interactive online databases TIMER and GEPIA2 were used to study the relationship between CKS1B expression and abundance of immune infiltration in tumors. B cells, CD4+ T cells, CD8+ T cells, and cancer-associated fibroblasts (CAFs) were selected as research parameters. XCELL, MCPCOUNTER, TIDE, and EPIC algorithms were applied for immune infiltration estimations.
The protein name “CKS1B” and organism “Homo sapiens” were entered into STRING website. The specific parameters were set as follows: network type (“full network”), meaning of network edges (“evidence”), active interaction sources (“experiments”), minimum required interaction score (“low confidence (0.40)”), maximum number of interactors to show (“no more than 50 interactors” in 1st shell). As a result, the available CKS1B binding proteins were identified. Subsequently, GeneMANIA was applied to do a protein interaction network. Next, Jvenn was used for cross-analysis to screen out common proteins and represented them as Venn diagram. In combination with KEGG (Kyoto Encyclopedia of Genes and Genomes), GO (Gene Ontology) database, and “ggplot2” R package, the enrichment pathway was obtained and visualized. Moreover, the heat maps of selected genes were provided by “Gene_Corr” module of TIMER2, which included cor and
The “TCGA Pan Cancer Atlas Studies” in “Quickselect” section of cBioPortal web was logged, and keyword “CKS1B” was entered to check the gene variation characteristics. The results of change frequency, mutation type, and CNA (copy number change) for all TCGA tumors were observed in “Cancer Type Summary” module. The mutation site information of CKS1B can be displayed in the schematic map of protein structure or 3D structure via the “Mutations” module. Kaplan-Meier plots with log-rank
The methylation status of CKS1B in tumor and adjacent normal tissues was assessed by DiseaseMeth database (version 2.0). The relationship between CKS1B expression and its DNA methylation was investigated using MEXPRESS database.
We first analyzed basal expression levels of CKS1B in different blood cells, tumor cell lines, and tumor tissues using Consensus database. As shown in Figure
Expression level of CKS1B in different tumors and its relationship with pathological stages. ((a) and (b)) CKS1B expression in different tumors based on ONCOMINE and UCSC XENA. (c) CKS1B mRNA expression in paired tumor tissues and normal tissues based on TCGA. (d) CKS1B protein expression in normal and diseased tissues of breast cancer, colon cancer, lung adenocarcinoma, ovarian cancer, clear cell RCC, and UCEC. (e) Representative immunohistochemistry images and detailed information of CKS1B expression in liver cancer, stomach cancer, ovary cancer tissues, and normal tissues based on THPA. (f) Correlations between CKS1B and tumor stages in ACC, HNSC, KICH, KIPR, LUAD, and PAAD patients based on GEPIA2.
Tumor cases were divided into high CKS1B expression group and low CKS1B expression group. The correlation between CKS1B and prognosis of patients with different tumors was studied by GEPIA2. As shown in Figures
Relationship between CKS1B and survival prognosis. (a) Overall survival and (b) disease-free survival of different tumors based on CKS1B expression level (GEPIA2). (c) Forest plot of multivariate Cox regression analysis of ACC patients. (d) Predictive value of CKS1B expression for diagnosis in LGG, LIHC, LUAD, PAAD, and STAD patients.
Expression levels of CKS1B in LAML and GEM tissue specimens. (a) RNA sequencing results in LAML showed CKS1B was not among the top 50 differentially expressed genes in the remission (CR) and nonremission (NR) groups after chemotherapy. Although specific data indicated CKS1B was higher in the NR group than that of the CR group (63.5 vs. 57.42), the results showed no statistical difference. (b) RT-qPCR results in GEM showed CKS1B mRNA in patients with good DFS was higher than that in patients with bad DFS.
In order to evaluate the clinical diagnostic value of CKS1B, we also calculated the area under ROC curve of LGG, LIHC (liver hepatocellular carcinoma), LUAD, PAAD, STAD, BRCA, COAD, ESCA (esophageal carcinoma), LUSC (lung squamous cell carcinoma), OV (ovarian serous cystadenocarcinoma), READ (rectum adenocarcinoma), KIRC, and GBM, most of which were above 0.9, indicating that CKS1B has high sensitivity and specificity for the diagnosis of these tumors (Figure
Immune system plays a crucial role in the occurrence, development, and treatment of tumors [
Relationship between CKS1B and tumor immune infiltration. ((a) and (b)) Correlation between CKS1B expression and cancer-associated fibroblasts infiltration levels based on different algorithms. (c) The heat map of the relationship between CKS1B and TILs in different tumors (red means positive correlation, and blue means negative correlation). ((d) and (e)) The heat maps of correlation between CKS1B and immunosuppressive factors and immunostimulatory factors. (f) CKS1B was positively associated with infiltrating levels of Act_CD4 in GEM and BLCA, Tgd in LGG, Th2 in SKCM, but negatively related to Th17 infiltrating in ACC and UCEC.
To further investigate the mechanism of CKS1B in tumorigenesis, we attempted to screen out the binding protein map targeting CKS1B by STRING tool (Figure
CKS1B-related gene enrichment analysis. (a) The binding protein map targeting CKS1B based on STRING tool. (b) Protein interaction network based on GeneMANIA database. (c) Venn diagram of the cross-analysis of above two results. (d) The expression of 10 screened common genes in various tumors. (e) GO enrichment and (f) KEGG enrichment analysis of CKS1B-related differentially expressed genes (DEGs). (g) Correlation analysis between CKS1B expression and screened common genes, including CDK1, CCNA2, CCNB1, CCNB2, CKS2, and SKP2. (h) Functional annotation of CKS1B-associated DEGs in ACC.
To specifically evaluate the function of CKS1B-related differentially expressed genes (DEGs), we used GSEA for enrichment analysis. As shown in Table
Gene set enrichment analysis of CKS1B.
Gene set name | NES | FDR | |
---|---|---|---|
KEGG_DRUG_METABOLISM_CYTOCHROME_P450 | -2.414 | 0.002 | 0.012 |
KEGG_RETINOL_METABOLISM | -2.368 | 0.002 | 0.012 |
KEGG_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 | -2.317 | 0.002 | 0.012 |
KEGG_STEROID_HORMONE_BIOSYNTHESIS | -2.289 | 0.002 | 0.012 |
KEGG_ASTHMA | -2.267 | 0.002 | 0.012 |
KEGG_ASCORBATE_AND_ALDARATE_METABOLISM | -2.252 | 0.002 | 0.012 |
REACTOME_GLUCURONIDATION | -2.225 | 0.002 | 0.012 |
REACTOME_PD_1_SIGNALING | -2.222 | 0.002 | 0.012 |
KEGG_ALLOGRAFT_REJECTION | -2.209 | 0.002 | 0.012 |
WP_PREGNANE_X_RECEPTOR_PATHWAY | -2.198 | 0.002 | 0.012 |
REACTOME_G2_M_CHECKPOINTS | 2.807 | 0.003 | 0.012 |
REACTOME_MITOTIC_G1_PHASE_AND_G1_S_TRANSITION | 2.819 | 0.003 | 0.012 |
REACTOME_MITOTIC_SPINDLE_CHECKPOINT | 2.85 | 0.003 | 0.012 |
REACTOME_RESOLUTION_OF_SISTER_CHROMATID_COHESION | 2.857 | 0.003 | 0.012 |
REACTOME_M_PHASE | 2.901 | 0.003 | 0.012 |
REACTOME_MITOTIC_PROMETAPHASE | 2.914 | 0.003 | 0.012 |
WP_CELL_CYCLE | 2.951 | 0.003 | 0.012 |
KEGG_CELL_CYCLE | 2.976 | 0.003 | 0.012 |
WP_RETINOBLASTOMA_GENE_IN_CANCER | 2.979 | 0.003 | 0.012 |
REACTOME_CELL_CYCLE_CHECKPOINTS | 3.11 | 0.003 | 0.012 |
The total frequency of CKS1B genetic alteration in patients with 33 tumor types was 3.54% (388/10953), and the top five tumors with the highest frequency were CHOL (cholangiocarcinoma) (16.67%), LIHC (11.56%), BRCA (9.5%), nonsmall cell lung cancer (9.19%), and UCEC (8.77%). On the contrary, CKS1B genetic variation was hardly observed in KIRC, leukemia, undifferentiated STAD, seminoma, nonseminomatous germ cell tumors, well-differentiated thyroid carcinoma, and ocular melanoma. “Amplification” was the most common type of genetic variation in all tumor cases. In addition, “mutation” in CHOL, COAD, HNSC (head and neck squamous carcinoma), and “structural variant” in pleural mesothelioma also had a high incidence (Figure
Mutation feature and prognosis significance of CKS1B in tumors. (a) The alteration frequency with mutation type, (b) mutation site, and (c) 3D structure of CKS1B. (d) The potential correlation between CKS1B mutation status and overall survival, disease-specific survival, disease-free, and progression-free survival of ACC based on cBioPortal tool. (e) The association between CKS1B copy number and mRNA expression.
Methylation analysis result in ACC demonstrated that CKS1B methylation was significantly lower in tumor than corresponding normal tissues (Figure
CKS1B DNA methylation analysis. (a) Differences of CKS1B methylation in ACC and corresponding normal tissues based on DiseaseMeth version 2.0. (b) The methylation sites of CKS1B DNA sequence were associated with gene expression based on MEXPRESS. CKS1B expression was illustrated by the blue line in the center of the plot. Pearson’s correlation coefficients and
Tumor mutation burden (TMB) is the total number of mutations per million bases in the coding region of gene exons that encode specific tumor cell proteins, including insertions, substitutions, deletions, and other forms of mutations [
Correlation analysis regarding the association of CKS1B expression and TMB.
Cancer type | Cor | Sig | |
---|---|---|---|
ACC | 0.451 | <0.001 | |
BLCA | 0.274 | <0.001 | |
BRCA | 0.344 | <0.001 | |
CESC | 0.089 | 0.134 | |
CHOL | 0.146 | 0.395 | |
COAD | 0.082 | 0.105 | |
DLBC | 0.097 | 0.568 | |
ESCA | -0.087 | 0.277 | |
GBM | 0.041 | 0.622 | |
HNSC | 0.207 | <0.001 | |
KICH | 0.399 | 0.001 | |
KIRC | 0.123 | 0.025 | |
KIRP | 0.086 | 0.155 | |
LAML | 0.141 | 0.272 | |
LGG | 0.415 | <0.001 | |
LIHC | 0.149 | 0.005 | |
LUAD | 0.483 | <0.001 | |
LUSC | 0.264 | <0.001 | |
MESO | 0.355 | 0.001 | |
OV | 0.233 | <0.001 | |
PAAD | 0.492 | <0.001 | |
PCPG | -0.054 | 0.472 | |
PRAD | 0.133 | 0.003 | |
READ | 0.207 | 0.017 | |
SARC | 0.301 | <0.001 | |
SKCM | 0.248 | <0.001 | |
STAD | 0.422 | <0.001 | |
TGCT | 0.118 | 0.159 | |
THCA | -0.025 | 0.582 | |
THYM | -0.433 | <0.001 | |
UCEC | 0.126 | 0.004 | |
UCS | 0.047 | 0.729 | |
UVM | -0.105 | 0.355 |
Correlation between CKS1B and TMB/MSI in different tumors. (a) Correlation between TMB and CKS1B expression. (b) Correlation between MSI and CKS1B expression.
Correlation analysis regarding the association of CKS1B expression and MSI.
Cancer type | Cor | Sig | |
---|---|---|---|
ACC | -0.107 | 0.349 | |
BLCA | 0.153 | 0.002 | |
BRCA | 0.063 | 0.045 | |
CESC | 0.025 | 0.669 | |
CHOL | 0.242 | 0.155 | |
COAD | 0.164 | 0.001 | |
DLBC | 0.344 | 0.017 | |
ESCA | 0.117 | 0.142 | |
GBM | 0.031 | 0.704 | |
HNSC | 0.266 | <0.001 | |
KICH | 0.075 | 0.553 | |
KIRC | 0.068 | 0.212 | |
KIRP | 0.142 | 0.017 | |
LAML | -0.278 | 0.022 | |
LGG | -0.049 | 0.270 | |
LIHC | 0.103 | 0.047 | |
LUAD | -0.045 | 0.312 | |
LUSC | -0.123 | 0.006 | |
MESO | -0.038 | 0.736 | |
OV | -0.053 | 0.384 | |
PAAD | 0.089 | 0.244 | |
PCPG | -0.021 | 0.779 | |
PRAD | -0.011 | 0.807 | |
READ | 0.158 | 0.052 | |
SARC | 0.277 | <0.001 | |
SKCM | 0.073 | 0.113 | |
STAD | 0.223 | <0.001 | |
TGCT | 0.009 | 0.913 | |
THCA | 0.15 | 0.001 | |
THYM | -0.018 | 0.842 | |
UCEC | 0.191 | <0.001 | |
UCS | 0.181 | 0.182 | |
UVM | 0.162 | 0.150 |
CKS1B, also known as cell cycle-dependent protease regulatory subunit, is a small molecule protein (9KD) encoded by CKS1 gene in the lq21 region of human chromosome and participate a lot of important physiological and pathological processes. Recently, more and more scholars have discovered that CKS1B is closely related to the occurrence and development of malignant tumors. For example, Fujita et al. found CKS1B protein was highly expressed in nonsmall cell lung cancer patients [
Our results revealed that although CKS1B was highly expressed in most tumors, its survival and prognostic significance varied among them. For example, high CKS1B expression was associated with poor OS and DFS in KIRP, LGG, LUAD, PAAD, and SKCM. In view of this, identifying high-risk patients as soon as possible, formulating personalized treatment plans, and strengthening regular follow-up of these patients are expected to improve their prognosis. However, CKS1B showed no correlation with OS of LUSC and LAML. More even, its high expression was related to favorable OS in RIRC and better DFS in GEM. Our RNA sequencing in LAML and RT-qPCR in GEM also confirmed this. While whether the current evidence based on databases could fully and truly reflect the prognostic significance of CKS1B in other tumors need to be further verified by more basic experiments.
We also investigated the relationship between CKS1B and TMB and MSI. It has been demonstrated that these two indicators can predict patient’s response to multiple drugs, especially immune checkpoint inhibitors [
Occurrence and progression of tumors are not only caused by genetic changes of tumor cells themselves but also the microenvironment also plays a key role in this process [
Our first pan-cancer analysis of CKS1B demonstrated a statistical association between CKS1B and tumor clinical prognosis, immune cell infiltration, DNA methylation, tumor mutation burden, and microsatellite instability across multiple tumors. It is helpful to understand the role of CKS1B from a holistic perspective. However, there are some limitations of our studies. In the future, we will focus on verifying these obtained data through basic experiments to better understand the mechanism and regulatory network of CKS1B.
Some of the original data can be obtained directly from TGGA, OCOMINE, and other databases, further inquiries (RNA sequencing and PCR data) can be directed to the corresponding author.
Informed consent was signed by all the participants.
The authors have declared that no competing interest exists.
This study was authorized by the Medical Ethics Committee of the Xiangya Hospital, Central South University. This work was supported by the National Natural Science Foundation of China (grant number 81600135).
Figure S1: CKS1B expression in different types of human tumors. The basal expression level of CKS1B in different (a) blood cells, (b) tumor cell lines, and (c) tumor tissues using Consensus database. (d) The expression of CKS1B in paired tumors and normal tissues of CHOL, ESCA, KIRP, READ, COADREAD, THCA, KICH, and PRAD. (e) Correlations between CKS1B and tumor stages in BRCA, LIHC, and THCA patients based on GEPIA2.